Quantitative Marketing and Economics

, Volume 14, Issue 4, pp 353–384 | Cite as

The palette that stands out: Color compositions of online curated visual UGC that attracts higher consumer interaction

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Abstract

Photos posted by consumers on social media, like Instagram, often include brands. Despite the substantial increase in such photos, there have been few investigations into how prospective consumers respond to this visual UGC. We begin to address this gap by investigating the role of the color compositions of visual UGC in consumer response. Consumer response is operationalized as the click-rate for a photo by a consumer when it is curated on the online site of the brand that it includes. This is the proportion of visitors who click on it for an enlarged view. Composition is operationalized as the specific combination of levels of the photo’s color attributes: hue, chroma, and brightness. Our goal is to identify the color compositions of photos, ceteris paribus, which get more clicks when they are curated. Data for our investigation comes from clicks over a one-year period on photos posted on Instagram curated by fifteen brands in six product categories on their sites. We assume Beta distributed proportions and calibrate a Beta regression using MCMC methods for our investigation.

We find that click-rates are higher for photos that include higher proportions of green and lower proportions of red and cyan. We also find that chroma of red and blue are higher in photos with higher click-rates. Findings from our research led the sponsoring firm to modify its proprietary curation algorithm for client brands. The firm informed us that, post-modification, there has been a substantial increase in click-rates of curated photos for brands in several categories.

Keywords

Visual UGC Color composition Click-rates Bayesian Beta regression 

References

  1. Alba, J., Lynch, J., Weitz, B., Janiszewski, C., Lutz, R., Sawyer, A., & Wood, S. (1997). Interactive home shopping: consumer, retailer, and manufacturer incentives to participate in electronic marketplaces. Journal of Marketing, 61(3), 38–53.CrossRefGoogle Scholar
  2. Anderson, E. T., & Simester, D. I. (2014). Reviews without a purchase: low ratings, loyal customers, and deception. Journal of Marketing Research, 51(3), 249–269.CrossRefGoogle Scholar
  3. Axelsson, Ö. (2007). Towards a psychology of photography: dimensions underlying aesthetic appeal of photographs. Perceptual and Motor Skills, 105(2), 411–434.CrossRefGoogle Scholar
  4. Bagchi, R., & Cheema, A. (2013). The effect of red background color on willingness-to-pay: the moderating role of selling mechanism. Journal of Consumer Research, 39(5), 947–960.CrossRefGoogle Scholar
  5. Bellizzi, J. A., & Hite, R. E. (1992). Environmental color, consumer feelings, and purchase likelihood. Psychology & Marketing, 9(5), 347–363.CrossRefGoogle Scholar
  6. Bellizzi, J. A., Crowley, A. E., & Hasty, R. W. (1983). The effects of color in store design. Journal of Retailing, 59(1), 21–45.Google Scholar
  7. Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205.CrossRefGoogle Scholar
  8. Blinn, J. F. (2005). What is a pixel? IEEE Computer Graphics and Applications, 25(5), 82–87.CrossRefGoogle Scholar
  9. Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing. MIT Sloan Management Review, 54(4), 23–29.Google Scholar
  10. Chandon, P., Hutchinson, J. W., Bradlow, E. T., & Young, S. H. (2009). Does In-store marketing work? Effects of the number and position of shelf facings on brand attention and evaluation at the point of purchase. Journal of Marketing, 73(6), 1–17.CrossRefGoogle Scholar
  11. Chen, Y., & Xie, J. (2008). Online consumer review: word-of-mouth as a new element of marketing communication mix. Management Science, 54(3), 477–491.CrossRefGoogle Scholar
  12. Chen, Y., Wang, Q., & Xie, J. (2011). Online social interactions: a natural experiment on word of mouth versus observational learning. Journal of Marketing Research, 48(2), 238–254.CrossRefGoogle Scholar
  13. Chevalier, M. (1975). Increase in sales due to In-store display. Journal of Marketing Research, 12(4), 426–431.CrossRefGoogle Scholar
  14. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: online book reviews. Journal of Marketing Research, 43(3), 345–354.CrossRefGoogle Scholar
  15. Chintagunta, P. K., Gopinath, S., & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5), 944–957.CrossRefGoogle Scholar
  16. Constant, D., Sproull, L., & Kiesler, S. (1996). The kindness of strangers: the usefulness of electronic weak ties for technical advice. Organization Science, 7(2), 119–135.CrossRefGoogle Scholar
  17. Curhan, R. C. (1974). The effects of merchandising and temporary promotional activities on the sales of fresh fruits and vegetables in supermarkets. Journal of Marketing Research, 11(3), 286–294.CrossRefGoogle Scholar
  18. De Bock, T., & Van Kerckhove, A. (2014). To contrast or not to contrast? Consumers’ response to color combinations. Advances in Consumer Research, 42, 716–719.Google Scholar
  19. Duan, W., Gu, B., & Whinston, A. B. (2008). The dynamics of online word-of-mouth and product sales—an empirical investigation of the movie industry. Journal of Retailing, 84(2), 233–242.CrossRefGoogle Scholar
  20. Eastwood, J. D., Smilek, D., & Merikle, P. M. (2001). Differential attentional guidance by unattended faces expressing positive and negative emotion. Perception & Psychophysics, 63(6), 1004–1013.CrossRefGoogle Scholar
  21. Edell, J. A., & Staelin, R. (1983). The information processing of pictures in print advertisements. Journal of Consumer Research, 10(1), 45–61.CrossRefGoogle Scholar
  22. Ferrari, S., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7), 799–815.CrossRefGoogle Scholar
  23. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., & Steele, D. (1995). Query by image and video content: the QBIC system. Computer, 28(9), 23–32.CrossRefGoogle Scholar
  24. Floyd, K., Freling, R., Alhoqail, S., Cho, H. Y., & Freling, T. (2014). How online product reviews affect retail sales: a meta-analysis. Journal of Retailing, 90(2), 217–232.CrossRefGoogle Scholar
  25. Gagnon, J. P., & Osterhaus, J. T. (1985). Effectiveness of floor displays on the sales of retail products. Journal of Retailing;Spring, 61(1), 104–116.Google Scholar
  26. Gatignon, H., & Robertson, T. S. (1985). A propositional inventory for new diffusion research. Journal of Consumer Research, 11(4), 849–867.CrossRefGoogle Scholar
  27. Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545–560.CrossRefGoogle Scholar
  28. Gorn, G. J., Chattopadhyay, A., Yi, T., & Dahl, D. W. (1997). Effects of color as an executional cue in advertising: They’re in the shade. Management Science, 43(10), 1387–1400.CrossRefGoogle Scholar
  29. Heiman, A., & Muller, E. (1996). Using demonstration to increase new product acceptance: controlling demonstration time. Journal of Marketing Research, 33(4), 422–430.CrossRefGoogle Scholar
  30. Herr, P. M. (1986). Consequences of priming: judgment and behavior. Journal of Personality and Social Psychology, 51(6), 1106–1115.CrossRefGoogle Scholar
  31. Herr, P. M. (1989). Priming price: prior knowledge and context effects. Journal of Consumer Research, 16(1), 67–75.CrossRefGoogle Scholar
  32. Hoegg, J., & Alba, J. W. (2007). Taste perception: more than meets the tongue. Journal of Consumer Research, 33(4), 490–498.CrossRefGoogle Scholar
  33. Itten, J. (1960). The art of color. New York: Van Nostrand Reinhold.Google Scholar
  34. Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions, Wiley series in probability and mathematical statistics: applied probability and statistics (2nd ed., Vol. 2). Wiley: New York.Google Scholar
  35. Journal, W. S. (2015). Pinterest’s Problem: Getting Men to Commit. .Retrieved from http://www.wsj.com/articles/pinterests-problem-getting-men-to-commit-1421944331
  36. Kaltcheva, V. D., & Weitz, B. A. (2006). When should a retailer create an exciting store environment? Journal of Marketing, 70(1), 107–118.CrossRefGoogle Scholar
  37. Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1–22.CrossRefGoogle Scholar
  38. Kisielius, J. (1982). The role of memory in understanding advertising media effectiveness: the effect of imagery on consumer decision making. Advances in Consumer Research, 9(1), 183–186.Google Scholar
  39. Kotler, P. (1973). Atmospherics as a marketing tool. Journal of Retailing, 49(4), 48–64.Google Scholar
  40. Kuehni, R. G. (2003). Color space and its divisions: color order from antiquity to the present. John Wiley & Sons.Google Scholar
  41. Labrecque, L. I., & Milne, G. R. (2012). Exciting red and competent blue: the importance of color in marketing. Journal of the Academy of Marketing Science, 40(5), 711–727.CrossRefGoogle Scholar
  42. Labrecque, L. I., & Milne, G. R. (2013). To be or not to be different: exploration of norms and benefits of color differentiation in the marketplace. Marketing Letters, 24(2), 165–176.CrossRefGoogle Scholar
  43. Lam, S. Y., & Mukherjee, A. (2005). The effects of merchandise coordination and juxtaposition on consumers’ product evaluation and purchase intention in store-based retailing. Journal of Retailing, 81(3), 231–250.CrossRefGoogle Scholar
  44. Lambrecht, A., & Tucker, C. (2013). When does retargeting work? Information specificity in online advertising. Journal of Marketing Research, 50(5), 561–576.CrossRefGoogle Scholar
  45. Larson, J. S., Bradlow, E. T., & Fader, P. S. (2005). An exploratory look at supermarket shopping paths. International Journal of Research in Marketing, 22(4), 395–414.CrossRefGoogle Scholar
  46. Lemmens, A., & Croux, C. (2006). Bagging and boosting classification trees to predict churn. Journal of Marketing Research, 43(2), 276–286.CrossRefGoogle Scholar
  47. Lichtenfeld, S., Elliot, A. J., Maier, M. A., & Pekrun, R. (2012). Fertile green green facilitates creative performance. Personality and Social Psychology Bulletin, 38(6), 784–797.CrossRefGoogle Scholar
  48. Liu, Y. (2006). Word of mouth for movies: its dynamics and impact on box office revenue. Journal of Marketing, 70(3), 74–89.CrossRefGoogle Scholar
  49. London, B., Stone, J., & Upton, J. (2011). Photography. New Jersey: Pearson Education, Inc.Google Scholar
  50. Mandel, N., & Johnson, E. J. (2002). When web pages influence choice: effects of visual primes on experts and novices. Journal of Consumer Research, 29(2), 235–245.CrossRefGoogle Scholar
  51. MATLAB version 8.2.0701. (2013). The MathWorks Inc.Google Scholar
  52. McNamara, T. P. (1992). Theories of priming: I. Associative distance and lag. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(6), 1173–1190.Google Scholar
  53. Mendelson, A. (2001). Effects of novelty in news photographs on attention and memory. Media Psychology, 3(2), 119–157.CrossRefGoogle Scholar
  54. Meyers-Levy, J., & Peracchio, L. A. (1992). Getting an angle in advertising: the effect of camera angle on product evaluations. Journal of Marketing Research, 29(4), 454–461. doi:10.2307/3172711.CrossRefGoogle Scholar
  55. Meyers-Levy, J., & Peracchio, L. A. (1995). Understanding the effects of color: how the correspondence between available and required resources affects attitudes. Journal of Consumer Research, 22(2), 121–138.CrossRefGoogle Scholar
  56. Miller, E. G., & Kahn, B. E. (2005). Shades of meaning: the effect of color and flavor names on consumer choice. Journal of Consumer Research, 32(1), 86–92.CrossRefGoogle Scholar
  57. Moe, W. W., & Trusov, M. (2011). The value of social dynamics in online product ratings forums. Journal of Marketing Research, 48(3), 444–456.CrossRefGoogle Scholar
  58. Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311–329.CrossRefGoogle Scholar
  59. Nelson, P. (1974). Advertising as information. Journal of Political Economy, 82(4), 729–754.CrossRefGoogle Scholar
  60. Othman, A., & Martinez, K. (2008). Colour appearance descriptors for image browsing and retrieval (Vol. 6820, p. 68200R–68200R–12).Google Scholar
  61. Peracchio, L. A., & Meyers-Levy, J. (2005). Using stylistic properties of ad pictures to communicate with consumers. Journal of Consumer Research, 32(1), 29–40.CrossRefGoogle Scholar
  62. Pieters, R., Wedel, M., & Batra, R. (2010). The stopping power of advertising: measures and effects of visual complexity. Journal of Marketing, 74(5), 48–60.CrossRefGoogle Scholar
  63. Putrevu, S., Tan, J., & Lord, K. R. (2004). Consumer responses to complex advertisements: the moderating role of need for cognition, knowledge, and gender. Journal of Current Issues & Research in Advertising, 26(1), 9–24.CrossRefGoogle Scholar
  64. Roullet, B., & Droulers, O. (2005). Pharmaceutical packaging color and drug expectancy. Advances in Consumer Research, 32(1), 164–171.Google Scholar
  65. Schloss, K. B., & Palmer, S. E. (2011). Aesthetic response to color combinations: preference, harmony, and similarity. Attention, Perception, & Psychophysics, 73(2), 551–571.CrossRefGoogle Scholar
  66. Schlosser, A. E. (2011). Can including pros and cons increase the helpfulness and persuasiveness of online reviews? The interactive effects of ratings and arguments. Journal of Consumer Psychology, 21(3), 226–239.CrossRefGoogle Scholar
  67. Shriver, S. K., Nair, H. S., & Hofstetter, R. (2013). Social ties and user-generated content: evidence from an online social network. Management Science, 59(6), 1425–1443.CrossRefGoogle Scholar
  68. Simonson, I., & Winer, R. S. (1992). The influence of purchase quantity and display format on consumer preference for variety. Journal of Consumer Research, 19(1), 133–138.CrossRefGoogle Scholar
  69. Sun, M. (2011). How does the variance of product ratings matter? Management Science, 58(4), 696–707.CrossRefGoogle Scholar
  70. The Atlantic (2012). A guide to the Instagram filters you’ll soon be seeing on Facebook. The Atlantic, 10.Google Scholar
  71. Valdez, P., & Mehrabian, A. (1994). Effects of color on emotions. Journal of Experimental Psychology: General, 123(4), 394.CrossRefGoogle Scholar
  72. Walls, H. J. (1959). How photography works. Focal Press.Google Scholar
  73. Walls, H. J., & Attridge, G. G. (1977). Basic photo science: how photography works. In London. New York: Focal Press.Google Scholar
  74. Wang, J., Hedar, A.-R., Wang, S., & Ma, J. (2012). Rough set and scatter search metaheuristic based feature selection for credit scoring. Expert Systems with Applications, 39(6), 6123–6128.CrossRefGoogle Scholar
  75. Wedel, M., & Pieters, R. (2007). Visual Marketing. New York: Lawrence Erlbaum Associates.Google Scholar
  76. Wedel, M., & Pieters, R. (2012). Visual marketing: from attention to action. Psychology Press.Google Scholar
  77. Wedel, M., & Pieters, R. (2014). The buffer effect: the role of color when advertising exposures are brief and blurred. Marketing Science, 34(1), 134–143.CrossRefGoogle Scholar
  78. Weiss, A. M., Lurie, N. H., & MacInnis, D. J. (2008). Listening to strangers: whose responses are valuable, how valuable are they, and why? Journal of Marketing Research, 45(4), 425–436.CrossRefGoogle Scholar
  79. Wilkinson, J. B., Mason, J. B., & Paksoy, C. H. (1982). Assessing the impact of short-term supermarket strategy variables. Journal of Marketing Research, 19(1), 72–86.CrossRefGoogle Scholar
  80. Yadav, M. S., & Pavlou, P. A. (2014). Marketing in Computer-Mediated Environments: research synthesis and new directions. Journal of Marketing, 78(1), 20–40.CrossRefGoogle Scholar
  81. Zhu, F., & Zhang, X. M. (2010). Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–148.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Belk College of BusinessUniversity of North Carolina-CharlotteCharlotteUSA
  2. 2.Lubar School of BusinessUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

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